Sparse Channel Reconstruction With Nonconvex Regularizer via DC Programming for Massive MIMO Systems

TL;DR

This paper introduces a nonconvex regularizer-based method for sparse channel reconstruction in massive MIMO systems, improving accuracy and reducing bias compared to traditional convex regularization techniques.

Contribution

It proposes a novel nonconvex regularizer and an efficient algorithm using DC programming for more accurate sparse channel estimation in massive MIMO systems.

Findings

Outperforms existing algorithms in reconstruction accuracy

Faster convergence and computational efficiency

Reduces bias in sparse channel estimation

Abstract

Sparse channel estimation for massive multiple-input multiple-output systems has drawn much attention in recent years. The required pilots are substantially reduced when the sparse channel state vectors can be reconstructed from a few numbers of measurements. A popular approach for sparse reconstruction is to solve the least-squares problem with a convex regularization. However, the convex regularizer is either too loose to force sparsity or lead to biased estimation. In this paper, the sparse channel reconstruction is solved by minimizing the least-squares objective with a nonconvex regularizer, which can exactly express the sparsity constraint and avoid introducing serious bias in the solution. A novel algorithm is proposed for solving the resulting nonconvex optimization via the difference of convex functions programming and the gradient projection descent. Simulation results show that the proposed algorithm is fast and accurate, and it outperforms the existing sparse recovery algorithms in terms of reconstruction errors.